Deep long short-term memory networks for nonlinear structural seismic response prediction

By Ruiyang Zhang, Zhao Chen, Su Chen, Jingwei Zheng, O. Buyukozturk, Hao Sun. Computers & structures, 2019

501 citations recorded by the source corpus.

Abstract This paper presents a comprehensive study on developing advanced deep learning approaches for nonlinear structural response modeling and prediction. Two schemes of the long short-term memory (LSTM) network are proposed for data-driven structural seismic response modeling. The proposed deep learning model, trained on available datasets, is capable of accurately predicting both elastic and inelastic response of building structures in a data-driven fashion as opposed to the classical physics-based nonlinear time history analysis using numerical methods. In addition, an unsupervised learning algorithm based on a proposed dynamic K-means clustering approach is established to cluster the seismic inputs in order to (1) generate the least but the most informative datasets for training the LSTM and (2) improve the prediction accuracy and robustness of the model trained with limited data. The performance of the proposed approach is successfully demonstrated through three proof-of-concept studies that include a nonlinear hysteretic system, a real-world building with field sensing data, and a steel moment resisting frame. The results show that the proposed LSTM network is a promising, reliable and computationally efficient approach for nonlinear structural response prediction, and offers significant potential in seismic fragility analysis of buildings for reliability assessment.

Subfields: Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/06b25d6128c70ee1386b13b6ca9f9145a5c6b6f1

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In Civil and Structural Engineering

Citations in the corpus

Physics-informed deep learning for incompressible laminar flows (2020, background): “Recent studies of leveraging DL to model physical system include, just to name a few, [2–6].”
Physics-Informed Multi-LSTM Networks for Metamodeling of Nonlinear Structures (2020, methodology): “For example, Zhang et al. [41] successfully developed a deep long short-term memory (LSTM) network for modeling of nonlinear seismic response of structures with large plastic deformation.”
Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights (2021, background): “The work by Zhang et al. 235 (Level 1-2,4) is of the ﬁ rst implementations of LSTM network with limited data for seismic response modeling of highly nonlinear complex dynamic systems.”
The promise of implementing machine learning in earthquake engineering: A state-of-the-art review (2020, background): “Recently, a DL approach, named as the long short-term memory (LSTM) network, was proposed by Zhang et al. (2019b) to model and predict the seismic responses of a nonlinear hysteretic system, a real-world building with field sensing data, and a steel moment resisting frame.”